In-Class Poster Session Assignment:
This assignment is a replacement of the “in-class presentation of research papers” that I have been
doing in the past. The idea and intention is still the same: to introduce the concept of finding,
studying and reporting research papers (or book chapters). This should provide a good training
excise for our students.
The date of the poster session is 4/29. Each of you is required to give a poster based on some
papers that you read. A partial list of (suggested) topics is attached below. Feel free to choose a
topic that is not on the list, but please let me know if you do so. Your posters can just follow one or
two papers (or, book sections). Try first to find the paper(s) yourselves, and I will be happy to help
you on this as well. Review papers are ideal for this type of excises, and master students can use
some data analysis oriented papers.
Your posters should be less than 8 pages (main texts about 28 to 40 font sizes), and should be
clearly structured and clearly stated in your own sentences. You are also required to interact with
other fellow students and write a one-page report on one of the posters from other people. You
need to hand in your poster after the poster session and hand in your one-page report at the due date
of the take-in exam.
On 4/29, we may also randomly pick several students (use random draws) and ask them to present
their posters to the fellow classmates --- so be prepared.
The grade will be based on your understanding on the topics/paper, the clarity of your poster and
your report, as well as your interaction with your fellow classmates.
Good luck!
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Partial list of (suggested) topics for in-class presentation.
I. Regression Models:
a) LASSO Regression
b) Bandwidth selection
c) Overview on some other smoothing techniques
d) Semiparametric regression
e) etc.
II. EM algorithm/Missing Data/Latent models
a) Further developments of EM algorithm
b) Censoring data in survival analysis
c) Quantal response theory (latent model)
d) etc.
III. Monte Carlo simulation/MCMC
a) Assessing convergence of Gibbs chain
b) Data Augmentation method
c) Monte-Carlo EM algorithms
d) etc.
IV. Estimating Equations/GEE/Longitudinal data
a) recent developments/Extensions of GEE models
b) Computing issues in Generalized Mixed Effects Models
c) Basic asymptotic results on related to estimating equations
d) etc.